Using the Shiny App
9/20/2021
Introduction
Shiny scExplorer is a tool for analyzing single-cell RNA-seq data collected from acute myeloid leukemia patients. The app can be used to visualize gene expression data and surface protein data to determine how patterns in expression vary between patients, between cell clusters, between response categories (sensitive vs. resistant patients), and between other metadata categories. The app also shows genes differentially expressed between metadata categories as an interactive table that can be grouped by different categories. Plots and tables in the app are created using the Seurat R package.
Creating Plots from Data
To create plots from the data, navigate to the plots tab. Plots will display in the main window according to the options specified in the bar on the left. At the top of the bar, you will see switches corresponding to each type of plot; check the box next to the corresponding plot type to view it in the main window. When a plot type is added, a tab with options specific to the plot will appear in the options bar. The tab may be opened or closed by clicking its header.
Feature Selection
If feature, violin, or dot plots are selected, a textbox will appear asking for features to include on the plots. Both genes and surface protein markers can be added here.
To add a gene or a surface protein marker, enter its name in the box. As you type, a menu of matching genes, surface markers, or both will appear. Select the desired feature name from the list to view it on the plots.
Multiple features can be added, and selected features will appear on all plots (except for the UMAP plot, which does not support display by feature). Click the “x” icon in the feature tag to remove it; alternately, you can press backspace when the text cursor is in front of the feature tag.
Feature names not on the list do not exist in the data being analyzed, though they may be present under an alternate name. The list may not immediately appear while the plots are updating; if this happens, please wait a few seconds for the updates to finish and the list to load. Feel free to contact us if the list does not load at all or takes too long to display options.
Separate features for dot plots
Dot plots are well-suited for visualizing larger numbers of features. To add additional features to the dot plot, check the “use separate features for dot plot” checkbox. A separate feature entry text box will appear, and features added in this box will only appear on the dot plot.
Grouping Plots by a Variable
UMAP, violin, and dot plots can be grouped by a given metadata type to aid in identifying trends based on the metadata variable. For UMAP plots, cells are colored based on their corresponding groups; for violin plots, a separate distribution is displayed for each group, and for dot plots, one dot is displayed per group for each gene. Feature plots do not support grouping since they are already colored according to the expression values of a specified gene. By default, all plots are grouped by clusters; this can be changed by opening the tab of specific options for the desired type and selecting a different option from the “metadata to group by” dropdown menu. Please see below for a detailed explanation of the metadata variables available.
Metadata Choices
Clusters: Cell clusters determined by Seurat and ClustifyR. Clusters generally group cells by type (primitive populations, monocytic populations, etc.), though the accuracy of clustering is not perfect and some cells may be incorrectly assigned.
Response: Description of the patient’s response to treatment (S= Sensitive, R= Resistant).
Treatment: The treatment assigned.
Patient ID: The unique identifier associated with each patient included in the dataset. Grouping by this variable will display data for each unique patient.
Splitting Plots by a variable
Metadata may also be used to “split” plots: setting a split by variable will divide the data into multiple plots based on the chosen metadata variable. For instance, splitting the data by response will create two plots, one showing the cells from resistant patients, and the other showing the cells from sensitive patients. Splitting can be performed on UMAP, feature, and violin plots. For UMAP and feature plots, selecting a split by variable will create a separate plot for each possible value in the variable (if there are two response categories, two separate plots will be created if “response” is chosen, and if there are 15 clusters, 15 separate plots will be created if “clusters” is chosen (not recommended for UMAP and feature plots)). For vioin plots, selecting a split by variable will divide the groups on the plot into subgroups based on the values of the split by variable. To split the metadata by a variable, open the tab of options specific to the desired plot type and select a variable from the “metadata to split by” dropdown menu. In the example below, selecting “clusters” as the split by variable, “response” as the group by variable, and “BCL2” as the selected feature segments the response groups into clusters, showing expression values for BCL2 in each cluster of cells from sensitive and resistant patients.
Downloading Plots
To download a plot, navigate to the tab of options for that plot, and click the download button at the bottom of the tab. The plot will be saved to your computer as an image in a .png format.
Changing the size of plots
The height and width of each plot may be adjusted by clicking “manually adjust plot dimensions” in the options tab specific to the plot. Use the sliders or the text boxes to adjust the width and height. To submit a value in the text box, press enter after entering the value in the box. For downloaded plot images, the ratio between the selected width and height will reflect the values entered, though the image size of the download will be larger to improve the quality of the downloaded image.
